Japanese Published Unexamined Patent Application No. 09-179883 discloses a method comprising the steps of: preparing a plane having two axes corresponding to two predicative numeric attributes of data in a database and divided into a plurality of rectangular buckets; storing the number of data included in each of the buckets so as to correspond to the bucket, as well as the number of data included in each of the buckets, whose true-false attribute value is true; segmenting a bucket region which is convex to one axis on the plane from the plane according to predetermined conditions, and deriving an association rule among the data using the segmented region. The object of this gazette is to derive the association rule among the data in the database. Since the region is constituted by a group of buckets connected to each other, the region is squarish in shape. A paper (paper 1: "Computing Optimized Rectilinear Regions for Association Rules," K. YODA, T. FUKUDA, Y. MORIMOTO, S.MORISHITA, and T. TOKUYAMA, in KDD-97 Proceedings Third International Conference on Knowledge Discovery and Data Mining, pp. 96-103, The AAAI Press, ISBN 0-1-57735-027-8) discloses a method for segmenting a region in a rectilinear convex, which comprises rectangular buckets, from a plane according to predetermined conditions unlike the above mentioned gazette. Also in this paper, it is intended to derive an association rule among data in a database. Since the region is defined by a group of rectangular buckets connected to each other, the region is squarish in shape.
Moreover, a paper (paper 2: "Efficient Construction of Regression Trees with Range and Region Splitting," Y. MORIMOTO, H. ISHII and S. MORISHITA, in Proceeding of the Twenty-third International Conference on Very Large Data Bases, pp 166-175, August 1997) discloses a method in a regression tree which comprises the steps of: preparing a plane having two axes corresponding to two predicative numeric attribute of data in a database and divided into a plurality of rectangular buckets; storing the number of data included in each of the buckets and a sum of an objective numeric attribute value of data so as to correspond to each bucket; segmenting a bucket region which minimizes the mean-squared error of the objective numeric attribute value from the plane; and generating a node concerning the data included in the segmented region and a node concerning data outside the region. The regression tree itself can be used for predicting a numeric attribute value in unknown data. However, since the bucket region which minimizes the mean-squared error of the objective numeric attribute value is the one which is convex to one axis on the plane or is rectilinear convex and is defined by a group of rectangular buckets connected to each other, the region is squarish in shape.